By continuing to use this site you consent to the use of cookies on your device as described in our cookie policy unless you have disabled them. You can change your cookie settings at any time but parts of our site will not function correctly without them. Policy Link

Topics

We interact with machine-learning systems every day. Without knowing it. From online adverts to the products that Amazon suggests. From the automated sales calls, to the voice recognition systems on our smartphones … these all use machine learning in some way.

This technology is transforming a broad spectrum of industries, even though it may be less obvious than with sporting predictions. In fact, most industries are already benefitting from machine learning.

Cars, doctors, and schools

One of the most publicly visible implementations of machine learning is the development of self-driving cars. Machine learning is, at its heart, advanced pattern recognition. In the example of self-driving cars, the system is taught to recognise elements of the landscape, such as a stop sign or traffic light, and act appropriately.

Each interaction with the environment adds more data to the system, allowing it to refine and improve the car’s driving ability. The more information the systems governing self-driving cars have about the way other vehicles, pedestrians, and animals behave, the better the system will become.

Even car manufacturing and servicing will benefit from machine learning in future. In this video, BMW shows how this type of data, gathered from RFID devices, is already helping them keep track of cars throughout their processes. Eventually, machine learning will help to predict and avoid production bottle necks before they occur, improving the services delivered to customers.

Eventually, machine learning may help to predict and avoid car manufacturing problems before they occur

In healthcare, IBM uses the massive computing power of its Watson technology to optimise oncology treatments for cancer patients. The system analyses the available data to find evidence-based treatment options and helps oncologists provide cancer patients with individualised treatment.

Of course, this underlines the importance of constant and reliable data streams feeding into the system. Watch this video to see how one hospital group,ISPPC in Belgium, uses the Internet of Things to collect medical and other streams of data to create integrated, centralised hospital management. Machine learning will in future add to hospitals’ ability to provide even more intuitive care to patients.

Machine learning may in future help hospital groups such as ISPPC analyse data to provide more intuitive patient care

Machine learning can also enhance the provision of quality education. In the US, 30% of first-year university students don’t return for their second year*. Through analysis of class attendance records, academic performance, and additional demographics, research has shown it’s possible to predict which students are most at risk. Interventions can then be put in place to assist them.

Old school steps up

Some of the greatest potential benefits of machine learning won’t come from Silicon Valley. Rather, it’s coming from the use of the technology by companies that provide services to the industrial, commercial, and agricultural sector, that have access to lots of data which can be leveraged to drive machine learning systems.

We’ve already seen examples of this with lift manufacturers leveraging sensor data to predict when break-downs are going to occur and schedule preventative maintenance. We’ve also seen farm equipment manufacturers starting to lay claim to the data generated by their equipment, providing them with the data needed to drive sophisticated machine-learning systems.

For any company looking to get real value out of their investment in machine learning, a few key elements need to be considered:

Firstly, data quality matters. The cleaner the data the better the quality of the intelligence that will be provided. The amount of data matters, as more data will help the system find patterns, but poor-quality data will be counter-productive.

Secondly, agility is vital. Machine learning systems don’t exist in a vacuum and, to produce the best results, they need to be constantly tweaked to achieve the desired outcomes.

Finally, understand what you want to achieve. Machine learning systems produce their best results when they are set against clear business objectives and a culture of data-driven transformation. Failure to link the investment in machine learning to your organisation’s strategy will result in diminished returns.

You may be interested in

Let’s start by saying that, much to my Welsh parent’s dismay, I’m not much of a sports fan! I’m a technologist who has little interest in watching a ball being kicked or tossed around a field and would much rather view strings of code working together in unison to form the perfect programming gameplay. I do however see a synergy between technology and sports and am both intrigued and excited by the way this relationship is steadily evolving.

Pro cycling is a thrilling sport, but watching it on TV has never been the most immersive experience for the casual fan who hasn’t donned lycra and been caught in the middle of a ‘live’ peloton themselves. Unlike Formula 1 broadcasts, where viewers can hear the scream of engines and see real-time data on each car’s speed or G-forces, cycle racing has largely avoided such bells and whistles.

I have a confession to make, well two actually – data doesn’t turn me on and I haven’t ridden a push bike on a road for some time. All the more reason to spend a couple of days with Dimension Data at the Tour de France.